Gesture is one of the key cues of social communication which includes movements of hands, head and other parts of human body that express various feelings, thoughts and emotions [1]}. Table REF  summarizes the attributes of widely used databases of emotional gestures. In this field, early studies were mostly built on acted or posed gestures. The Tilburg University Stimulus set [2]} collected photographic still images of 50 actors enacting different emotions. FABO database [3]} is one of the pioneer work which proposed using video clips of posed prototype gestures to recognize emotions. These videos were labelled with six basic emotions, as well as four more states, namely, neutral, anxiety, boredom, and uncertainty. Following that posed behavior which was captured in controlled recording conditions, researchers extended emotional gesture analysis into many directions. In HUMAINE [4]}, [5]}, the researchers elicited emotions via interaction with computer avatar of its operator. The Geneva multi-modal emotion portrayals (GEMEP) database [6]} contains more than 7 000 audio-video portrayals of 18 emotions portrayed by 10 actors. Also in a controlled setting, the subset [7]} of LIRIS-ACCEDE database [8]} collected upper body emotional gestures from 64 subjects. Using a Kinect sensor, Saha et al. [9]} collected 3D skeleton gesture data of 10 subjects, which included five induced emotions, i.e., anger, fear, happiness, sadness, and relaxation. Psaltis et al. [10]} collected skeletal gestural expressions that frequently appear in a game-play scenario. Similarly, the emoFBVP [11]} dataset has a multi-modal recordings of actors including body gestures with skeletal tracking. Emilya [12]} dataset captured 3D body movements of posed emotions via a motion capture system.
